advantages of python

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Python advantages and disadvantages

Let’s first dive into the advantages of Python.

Advantages of Python

1. Easy to Read, Learn and Write

Python is a high-level programming language that has English-like syntax.

This makes it easier to read and understand the code.

Python is really easy to pick up and learn, that is why a lot of people recommend Python to beginners.machine learning project spam detector

You need less lines of code to perform the same task as compared to other major languages like C/C++ and Java.

2. Improved Productivity

Python is a very productive language.

Due to the simplicity of Python, developers can focus on solving the problem.

They don’t need to spend too much time in understanding the syntax or behavior of the programming language.

You write less code and get more things done.

3. Interpreted Language

Python is an interpreted language which means that Python directly executes the code line by line.

In case of any error, it stops further execution and reports back the error which has occurred.

Python shows only one error even if the program has multiple errors.

This makes debugging easier.

4. Dynamically Typed

Python doesn’t know the type of variable until we run the code.

It automatically assigns the data type during execution.

The programmer doesn’t need to worry about declaring variables and their data types.

5. Free and Open-Source

Python comes under the OSI approved open-source license.

This makes it free to use and distribute.

You can download the source code, modify it and even distribute your version of Python.

This is useful for organizations that want to modify some specific behavior and use their version for development.

6. Vast Libraries Support

The standard library of Python is huge, you can find almost all the functions needed for your task.

So, you don’t have to depend on external libraries.

But even if you do, a Python package manager (pip) makes things easier to import other great packages from the Python package index (PyPi).

It consists of over 200,000 packages.

7. Portability

In many languages like C/C++, you need to change your code to run the program on different platforms.

That is not the same with Python.

You only write once and run it anywhere.

However, you should be careful not to include any system-dependent features.

Disadvantages of Python

1. Slow Speed

We discussed above that Python is an interpreted language and dynamically-typed language.

The line by line execution of code often leads to slow execution.

The dynamic nature of Python is also responsible for the slow speed of Python because it has to do the extra work while executing code.

So, Python is not used for purposes where speed is an important aspect of the project.

2. Not Memory Efficient

To provide simplicity to the developer, Python has to do a little tradeoff.

The Python programming language uses a large amount of memory.

This can be a disadvantage while building applications when we prefer memory optimization.

3. Weak in Mobile Computing

Python is generally used in server-side programming.

We don’t get to see Python on the client-side or mobile applications because of the following reasons.

Python is not memory efficient and it has slow processing power as compared to other languages.

4. Database Access

Programming in Python is easy and stress-free.

But when we are interacting with the database, it lacks behind.

The Python’s database access layer is primitive and underdeveloped in comparison to the popular technologies like JDBC and ODBC.

Huge enterprises need smooth interaction of complex legacy data and Python is thus rarely used in enterprises.

5. Runtime Errors

As we know Python is a dynamically typed language so the data type of a variable can change anytime.

A variable containing integer number may hold a string in the future, which can lead to Runtime Errors.

Therefore Python programmers need to perform thorough testing of the applications.


Python is a simpleversatile and a complete programming language.

It is a great choice for beginners up to professionals.

Although it has some disadvantages, we can observe that the advantages exceed the disadvantages.

Even Google has made Python one of its primary programming languages.

I hope that our article was useful to you.

Tags: advantages of pythondatabase accessdisadvantages of pythoninterpreted languagepython advantages and disadvantages


  1. Naveen February 28, 2020 at 1:18 pmIf we use database’s like noSQL(mongodb) then it comes to advantage rightReply
  2. pothireddy sireesha April 23, 2020 at 5:50 pmIn disadvantages you mentioned that The Python programming language uses a large amount of memory.Can you give example for that.Reply
  3. LukeNukem August 26, 2020 at 5:10 amHello,please excuse me: your article is great, but then I have some small remarks about my very own experiences.Python has DB-API 2.0, which is very similar to JDBC and ODBC. It then has many object-relational mappers like SQLAlchemy or SQLObject, and it even has Drivers for ODBC and JDBC. Also it has strong APIs for other database-style data managers like MongoDB, Elasticsearch or Redis, often developed and maintained by the developers of the software.Then, Python is often used within the scientific community. There are interfaces to many popular machine learning technologies like Tensorflow or Keras, and to most of the widespread frameworks for Distributed Computing, like HADOOP for batching, Apache Spark and others for microbatching, Apache Storm, Gearpump and Heroku for real-time stream processing… All this universe of software (and I’ve mentioned just a few that I tried out or worked with) is just to compute big data in as short a time as possible. This is not possible if Python can not efficiently get data that from nearly any sort of database, or write computed data back to those data stores.I also have other experience as to the performance of Python. You are absolutely correct that Python can be less performant than other languages. But that depends on so many things for real use cases, and on the tools you use. Most, if not all, benchmarks and performance community seem to focus on the “official” standard Python interpreter, called CPython. But there is more — a lot more. For example, there is a Python interpreter implemented in Java, called Jython, which can use native Java classes and objects. Similar goes for IronPython, an Implementation in Microsoft .NjET and able to use any native .NET feature.And then, there are other beasts. You can compile Python, yes, to real machine code. Nuitka compiles your python code into a C source code and compiles that C source into a real binary executable for your operating system. And I have been very surprised and very positively surprised to see what is possible when playing with that. You can access native C functions such as system functions via CFFI even without using these possibilities. You can integrate Python into other programs, for example with CPythons APIs or Boost::Python in the Boost C++ project. You can integrate other programs into Python.There are large libraries available for Python: you can have numpy, which is implemented in C and very fast, and vou van have very powerful data libraries like Pandas or the SciKits. Many of these are written in C and / or C++, numpy is said to even uses some old industry-standard Fortran code that other tools like Matlab or R also make use of. Matplotlib (or Pandas’), Bokeh, or the APIs of D3 offer great possibilities to nicely visualize data. And then… there is the final enemy of all Python interpreters, and truly, the best one comes at the end. Because there is another Python interpreter, and it rocks!The PyPy interpreter is — contrary to its standard counterpart CPython — optimized for performance, and it is a Just-In-Time (JIT) compiler. Today I’ve had some fun with very simple performance testing with regards to these Python interpreters. To make it short: the performance difference was enormous, many times more than I had expected. So I set up a simple web application with the Flask Framework. In that, there is the usual Flask boilerplate, two templates one inheriting from the other, and a function that is decorated to be mapped onto the “/” URL endpoint, rendering a small 4×2 array of data into the child template. The machine I used is a Dell E5580 Laptop with I7-7820HQ, 32 GB RAM with Kubuntu 18.04 LTS. All the tests where accomplished with ab(1) from the Apache httpd project with 10.000 requests and variable concurrencies of 1, 2, 4, 8, 16, 32 and 64.See how surprised I was: 😯In fact, CPython 3.6.9 needed roundabout 5,5 seconds to run under the built-in development server. With GUnicorn, it became a bit better and only needed 4.4 seconds.But then I tested GUnicorn with the PyPy3 interpreter both with its default sync MPM, but also with multiprocessing. Both provide excellent results and are nearly 10 times faster than the standard interpreter: for both of the MPM models, they provide excellent performance and run the aforementioned ab(1) test within little more than 0.6 seconds, but you need to warm up the JIT a bit… 😉However, for sure these are just indications, that don’t say anything about databases (if I had included one, I would rather have measured its performance) and other aspects. But the PyPy interpreter I have measured versus CPython for this special use case shows what the technology is capable of. Nearly ten times faster, isn’t that good?

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